Gray scale images of various documents are often stored, on microfilm for subsequent retrieval in order to conserve storage space by advantageously eliminating the need to store bulky originals. Retrieving (accessing) a microfilmed image of a document, on a manual basis, typically requires locating a desired roll of microfilm that houses the desired image, loading the roll into a manual reader and then advancing the microfilm to a desired frame at which the image is located. Thereafter, the image is optically enlarged and displayed on the reader.
Now, to minimize image retrieval time, particularly for archives that store a substantial number of documents, and also to permit the retrieved image to be electronically enhanced and processed, image management systems have been developed in the art. These systems are typified, for example, by the Kodak Image Management System (KIMS) system currently manufactured by the present assignee (KIMS is a trademark of the Eastman Kodak Company). Essentially, the KIMS system first locates the desired microfilm roll and frame through a computerized database inquiry. Then, an automated microfilm reader, i.e. a so-called film library also known as an autoloader, operating under computer control, fetches and then loads the desired roll into the reader. Once this has occurred, the film library automatically advances the roll to the desired frame. Thereafter, the film library electronically scans and digitizes a gray scale microfilm image present at the desired frame, and finally applies the resulting digitized bi-tonal image onto a local area network for storage, display and/or printing.
Within the KIMS system, the microfilm image of a document is scanned one line at a time by a microfilm scanner and specifically by a 2048-by-1 cell charge coupled device (CCD) array located therein. The array is positioned directly over (or in front of) the microfilmed image and is oriented substantially perpendicular to a direction through which the microfilm moves through the reader. A lamp situated below (or behind) the microfilm projects light through the image. As a result, each cell of the CCD array provides an analog signal which is proportional to the intensity of the light that passes through that portion of the scanned area of the microfilm which lies directly below (or behind) that element. The scanned area is generally 25 to 50% larger than the actual image of the document. In this manner, the full image is scanned even if a document used to form the image was photographed somewhat off center or tilted and/or if the CCD becomes slightly horizontally mis-aligned with respect to the microfilm. Overscanning is necessary particularly where rotary microfilmers have been used to photograph images onto microfilm. These filmers cause a wide variation in the location and orientation of the photographed document within a standard image area. Now, as each pixel (pel) is scanned, the analog signal corresponding to the intensity of that pixel is converted into a six bit digital signal which itself is subsequently thresholded to provide a single bit binary output signal for that pixel. All the single bit binary values are stored in a frame store memory. Once scanning has been completed, then the contents of the frame store memory which store the scanned image of the document are routed to a video compressor. Thereafter, the compressed bi-tonal image is sent to the local area network for storage, display and/or printing by downstream electronic processing equipment.
Typically, a scanned bi-tonal microfilmed image which appears on the network may be deficient in quality. This deficiency can result primarily from image noise and/or edge discrepancies.
Image noise takes the form of undesirable pixel transitions (i.e. from white to black, or black to white) occurring in the image. Therefore, to improve image quality and increase compressibility, these undesirable pixel transitions must be removed from the image. Image compressibility increases as the number of pixel transitions decreases. However, fine detail, such as small characters, typically spans a relatively small number of pixels. Therefore, as increasingly larger groups of isolated pixels are removed as noise, compressibility rises but fine detail is removed from the image and hence image quality degrades. Consequently, a tradeoff exists between image quality and compressibility in determining the size of isolated pixel groups that are to be removed, as noise, during a process of image enhancement.
To achieve a good compromise between image quality and compressibility, image noise is typically viewed as being a single isolated "on" (black) pixel occurring anywhere in the image. Any larger sized pixel groups are viewed as desirable detail which are to be left in the image. Image noise typically originates from any one of three sources: so-called paper noise existing in the document itself that has been microfilmed, so-called film noise caused by the grain size in the microfilm medium and electronic noise generated by the electronic scanning system.
Now, to properly threshold an image in order to remove image noise, a threshold level must intersect video pulses that form textual characters, in the scanned image, at a point that occurs above a pre-defined noise amplitude but below the peak amplitudes of these pulses. One technique for sensing the proper noise amplitude is to observe the occurrence of single isolated pixels that occur throughout the scanned image. Specifically, in the KIMS system, a microfilm image is scanned at a typical density of 200 pixels to the inch (approximately 79 pixel/cm). Noise generally takes the form of an isolated pixel of one color, i.e. black, surrounded by pixels of another color, i.e. white. A pixel of this size is simply not readily visible to an average reader. As such, a single isolated pixel does not form any part of a textual character but is instead noise. Single pixel noise can be one-dimensionally isolated wherein a single pixel is "on" while its neighbors to its left and right are both "off", or two-dimensionally isolated wherein the single pixel is "on" while its neighbors to the left, right, above and below and possibly also those which are diagonally oriented to the single pixel are all "off".
Ideally, then, one could set the threshold level to an appropriate level to filter out single pixel noise from the six bit digitized video. Unfortunately, in practice, single pixel noise can occur at various intensity levels throughout the full range of video amplitude in the scanned image thereby significantly complicating the process of thresholding. Specifically, first, single pixel noise can occur on the peaks of the video signal which correspond to detected pixels that form a textual character that appears on a background having the highest contrast, e.g. those pixels that form a black character on a white background. If the threshold were to be set to this level, then disadvantageously any character that is situated on a background having a reduced contrast, e.g. a white character on a gray background, would be entirely removed from the image. Specifically, the pixels which form these characters would not possess as high a peak amplitude value as those which form characters situated on the highest contrasting background and hence would be removed by the thresholding operation. Consequently, thresholding at this level would produce erroneous pixel patterns and hence incorrect text. Second, single pixel noise can also occur just above the level of the background video. The background level in the video signal corresponds to the proper background level of the scanned image. Single pixel noise which occurs here accurately indicates noise amplitude in the scanned image. Hence, setting a threshold value slightly above the background level will result in accurate thresholding of image noise. Third, single pixel noise can occur below the background level of the video signal. If this were to be used as a threshold value, then an excessive amount of noise would remain in the image and hence adversely affect image compressibility.
Now, since the background level may vary significantly across any line in the scanned image, the threshold level must track the background level and be dynamically maintained at a value slightly greater than the background level.
Edge discrepancies, the second primary cause of image quality degradation, often occur whenever an image is drastically reduced in size, such as through microfilming, which, by its very nature, eliminates a great deal of information from an original image. In particular, microfilm possesses a finite resolution, as does all photographic media, which tends to limit the size of the detail that can be photographed on the microfilm. If fine detail exists in a document and is reduced to a size which is smaller than this resolution, then this detail will be blurred in the microfilmed image and hence will appear blurred in any image that results from scanning the microfilmed image. Consequently, image edges in a scanned microfilmed image may often appear ragged and/or blurred instead of straight. Therefore, to improve image quality, all image edges should be sharpened during image enhancement.
Therefore, a need exists in the art for a system that enhances the quality of a multi-bit scanned image, particularly one resulting from a scanned microfilmed image and then accurately thresholds each pixel in the bi-tonal image into a single bit binary value. This system would enhance the image by both removing image noise therefrom and sharpening the edges of the image. Such a system would advantageously find particular use in improving the quality of images generated by an image management system.